Most current natural language understanding and processing systems use linguistic methods for the understanding of input sentences. The problem with linguistic models is that there has yet been no portable linguistic approach that can provide natural language translation at a satisfiable level of accuracy. The reason for this poor accuracy is that linguistic approaches require domain experts to customize the grammars and actions, and hence can take years to develop.
Statistically-based natural language understanding ("NLU") has recently been attempted, but no commercially viable systems have yet been made available. There are natural language ("NL") interfaces to databases, but their success has been limited due to their inaccuracy. One problem with these prior statistical methods is that they do not adequately model the notion of alignments between English and related semantic concepts. Without a doubt, since a NL interface to computers and databases is by definition large vocabulary, one will need an accurate statistical technique for processing NLU queries.